Open AccessClassifying electrical activity of the brain during imaginary movements of untrained subjects using artificial neural networks
Author(s) -
Semen A. Kurkin,
Elena Pitsik,
Alexandr Khramov
Publication year - 2020
Publication title -
informacionno-upravlâûŝie sistemy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.202
H-Index - 6
eISSN - 2541-8610
pISSN - 1684-8853
DOI - 10.31799/1684-8853-2019-6-77-84
Subject(s) - motor imagery , brain–computer interface , artificial intelligence , computer science , artificial neural network , electroencephalography , perceptron , pattern recognition (psychology) , radial basis function , support vector machine , multilayer perceptron , machine learning , psychology , neuroscience
Developing new classification methods for human brain electrical activity patterns corresponding to actual movements or motor imagery is an essential interdisciplinary problem in brain-computer interface research. One of the most promising approaches is the development of methods based on artificial neural networks. Purpose: The development of ANN-based methods for classifying electroencephalographic patterns associated with motor imagery in untrained subjects. Methods: Classifiers based on linear neural networks, multi-layer perceptrons, radial basis function networks and support vector machines. Results: The authors selected the optimal type, topology, learning algorithms and parameters of an artificial neural network in order to provide the most accurate and fast classification of lower limb motor imagery EEG signals. It has been studied how the number of the analyzed channels of a multichannel EEG and their choice affect the quality of motor imagery patterns classification. Optimal configurations were obtained for the electrode arrangements. The influence of EEG pre-processing on the accuracy of motor imagery recognition was analyzed. A computational experiment showed the accuracy of 90-95% in untrained subjects. Radial basis function network demonstrated the best performance. Besides, the dataset dimensionality has been significantly reduced down to 6–12 channels without any classification accuracy loss. Practical relevance: The obtained results can be useful for the developers of motor imagery EEG classification algorithms used in brain-computer interfaces.